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What is a Decision Support System (DSS) and why your company should adopt one

Michele Compare

In today’s fast-paced and data-driven world, making informed and timely decisions is crucial for businesses to gain a competitive edge. 

Decision Support Systems (DSS) are computer-based information systems designed to assist decision-makers in analyzing complex problems and facilitating the decision-making process. 

They provide valuable insights and analytical tools, playing a pivotal role in helping businesses navigate complex challenges and seize opportunities. 

These systems include data management, analytical models, and user interfaces, to facilitate the decision-making process.

DSS can be classified as 

  • model-driven: mathematical and analytical models to evaluate different scenarios and predict outcomes
  • data-driven, analyzing historical and real-time data to identify patterns and trends
  • knowledge-driven, leveraging expert knowledge and rules to provide decision-makers with recommendations and insights

However, with the advent of Artificial Intelligence and advancements in data management, DSS have undergone significant enhancements, revolutionizing the way organizations approach decision-making.

Artificial Intelligence has emerged as a game-changer in decision support systems, empowering organizations to harness the power of advanced analytics, machine learning, and natural language processing. 

AI algorithms can analyze vast amounts of data, uncover hidden patterns, and generate valuable insights that help decision-makers make well-informed choices.

AI-powered DSS can provide real-time recommendations, identify trends, and simulate various scenarios to assess potential outcomes, enabling businesses to make proactive decisions based on data-driven insights.


AI and Data Management to enhance DSSs

The success of a DSS lies in its ability to manage huge volumes of data.

Effective data management practices ensure that decision-makers have access to accurate, relevant, and up-to-date information for analysis. Data management involves activities such as data collection, cleansing, integration, storage, and retrieval.

With the proliferation of big data, organizations face the challenge of handling vast volumes of structured and unstructured data from various sources.

By implementing robust data management strategies – such as data warehousing, data lakes and data governance frameworks – organizations can ensure data consistency, integrity, and security, enabling decision-makers to trust the information provided by DSS. 

Moreover, data management practices also facilitate effective data sharing and collaboration among stakeholders, enhancing the overall decision-making process.

AI can enhance data management by automating data cleansing, classification, and integration tasks, reducing human effort and improving data quality. Additionally, AI-powered data analytics tools can detect anomalies, identify data patterns, and provide real-time data monitoring, enabling organizations to respond swiftly to changing circumstances.

Furthermore, AI-driven personalization features in DSS can tailor recommendations and insights to individual decision-makers, considering their preferences and historical data. This personalized approach increases user engagement and fosters more effective decision-making.


Making DSSs even more efficient

The unique selling point of Aramix’s DSSs lies in the ability to integrate Machine Learning, Artificial Intelligence and statistical methods with decision analysis techniques and a solid industrial engineering background, proposing decisions capable of solving the identified business and operation problems.

These models are based on the prediction of the asset’s future usage and operating conditions, both endogenous and exogenous.

Our DSSs encode all the processes that lead from the acquisition of the knowledge, information and data available – the so called KID methodology –  up to the suggestion and implementation of decisions on business processes.


A step further: recommendation systems

We also build recommendation engines and systems: advanced algorithms that analyze the user’s previous behavior to suggest those specific items they are likely to prefer, using  data analysis techniques.

Recommendation systems allow you to drive much higher conversions and enhance average order value, funneling multiple data sets – such as historical data, real-time visitor behavior, and alternative data – into a recommendation algorithm.

This is well beyond Business Intelligence: while BI collects data, makes it usable and useful, our DSSs makes it valuable for companies.